Distinguishing between renal oncocytoma and chromophobe renal cell carcinoma using raman molecular imaging

ABSTRACT

A system and method to provide a diagnosis of the renal disease state of a test renal sample. A database containing a plurality of reference Raman data sets is provided where each reference Raman data set has an associated known renal sample and an associated known renal disease state. A test renal sample is irradiated with substantially monochromatic light to generate scattered photons resulting in a test Raman data set. The test Raman data set is compared to the plurality of reference Raman data sets using a chemometric technique. Based on the comparison, a diagnosis of a renal disease state of the test renal sample is provided. The renal disease state includes renal oncocytoma or chromophobe renal carcinoma disease state.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/971,940, filed Sep. 13, 2007, entitled “Distinguishing Between RenalOncocytoma and Chromophobe Renal Cell Carcinoma Using Raman MolecularImaging” which is incorporated herein by reference in its entirety. Thisapplication is also continuation-in-part of U.S. patent application Ser.No. 12/070,010, filed Feb. 14, 2008, entitled “Spectroscopic System andMethod for Prediction Disease Outcome” which claims the benefit of U.S.Provisional Application No. 60/901,497, filed Feb. 14, 2007, entitled“Method for Using Raman Scattered Light to Predict Clinical Outcome ofDisease for Tissue Sample,” and U.S. Provisional Application No.60/896,236, filed Mar. 21, 2007, entitled “Spectroscopic System andMethod for Predicting Progressive Outcome of Cancer Patients” each ofwhich is incorporated herein by reference in its entirety.

FIELD OF DISCLOSURE

The present disclosure relates to methods and systems to use Ramanspectroscopy to identify the disease state of renal samples.

BACKGROUND

The biochemical composition of a cell is a complex mix of biologicalmolecules including, but not limited to, proteins, nucleic acids,lipids, and carbohydrates. The composition and interaction of thebiological molecules determines the metabolic state of a cell. Themetabolic state of the cell will dictate the type of cell and itsfunction (i.e., red blood cell, epithelial cell, etc.). Tissue isgenerally understood to mean a group of cells that work together toperform a function. Raman spectroscopic techniques provide informationabout the biological molecules contained in cells and tissues andtherefore provide information about the metabolic state. As the cell'sor tissue's metabolic state changes from the normal state to a diseasedstate, Raman spectroscopic techniques can provide information toindicate the metabolic change and therefore serve to diagnose andpredict the outcome of a disease. Cancer is a prevalent disease, sophysicians are very concerned with being able to accurately diagnosecancer and to determine the best course of treatment.

Raman spectroscopy may be explored for detection of various types ofdiseases in particular cancers. Because Raman spectroscopy is based onirradiation of a sample and detection of scattered radiation, it can beemployed non-invasively to analyze biological samples in situ. Thus,little or no sample preparation is required. Raman spectroscopytechniques can be readily performed in aqueous environments becausewater exhibits very little, but predictable, Raman scattering. It isparticularly amenable to in vivo measurements as the powers andexcitation wavelengths used are non-destructive to the tissue and have arelatively large penetration depth.

Raman Molecular Imaging (RMI) is a reagentless tissue imaging approachbased on the scattering of laser light from tissue samples. The approachyields an image of a sample wherein each pixel of the image is the Ramanspectrum of the sample at the corresponding location. The Raman spectrumcarries information about the local chemical environment of the sampleat each location. RMI has a spatial resolving power of approximately 250nm and can potentially provide qualitative and quantitative imageinformation based on molecular composition and morphology.

The vast majority of diseases, in particular cancer cases, arepathologically diagnosed using tissue from a biopsy specimen. Anexperienced pathologist can provide diagnostic information used to makemanagement decisions for the treatment of the cancer. In the case ofrenal disease, the diagnosis of renal oncocytoma (OC) and chromophoberenal cell carcinoma (ChRCC) based on histological features can often bechallenging. Currently, there are no reliable immunohistochemicalmarkers which separate these two neoplasms. Because OC is benign andChRCC may behave aggressively, the correct identification of thesediseases is crucial. A reliable method for distinguishing between OC andChRCC may assist clinicians to improve prognoses and treatment optionsfor patients with kidney disease.

Therefore it is desirable to devise methodologies that use Ramanspectroscopy techniques to differentiate various cell types (e.g.,normal, malignant, benign, etc.), to classify biological samples underinvestigation (e.g., a normal tissue, a diseased tissue, renaloncocytomas disease state and chromophobe renal cell carcinoma diseasestate), and to also predict clinical outcome (e.g., progressive ornon-progressive state of cancer, etc.) of a diseased cell or tissue.

SUMMARY

The present disclosure provides for a method to provide a diagnosis ofthe renal disease state of a test renal sample. A group of known renalsamples is provided where each known renal sample has an associatedknown renal disease state. The known renal disease state includes arenal oncocytoma disease state or a chromophobe renal cell carcinomadisease state. A Raman data set is obtained from each known renalsample. Each Raman data set is analyzed to identify a renal oncocytomareference Raman data set or chromophobe renal cell carcinoma referenceRaman data set depending on whether respective known renal sample is arenal oncocytoma sample or a chromophobe renal cell carcinoma sample. Afirst database is generated containing all renal oncocytoma referenceRaman data sets. A second database is generated containing allchromophobe renal cell carcinoma reference Raman data sets. A test Ramandata set of a test renal sample is obtained where the test renal samplehas an unknown renal disease state. A diagnostic of whether the testrenal sample has a renal oncocytoma disease state or a chromophobe renalcell carcinoma disease state is provided by comparing the test Ramandata set against the reference Raman data sets in then first referenceRaman and the second reference Raman databases using a chemometrictechnique.

The present disclosure further provides for yet another method toprovide a diagnosis of the renal disease state of a test renal sample. Adatabase containing a plurality of reference Raman data sets is providedwhere each reference Raman data set has an associated known renal sampleand an associated known renal disease state. A test renal sample isirradiated with substantially monochromatic light to thereby generatescattered photons. A test Raman data set is collected based on thescattered photons. The test Raman data set is compared to the pluralityof reference Raman data sets using a chemometric technique. Based on thecomparison, a diagnosis of a renal disease state of the test renalsample is provided.

In one such embodiment, the known renal disease state includes a renaloncocytoma disease state or a chromophobe renal cell carcinoma diseasestate.

In another such embodiment, the reference Raman data sets includes aplurality of reference Raman spectra obtained from the one or moreregions of interest of the known renal sample.

In still another such embodiment, the test Raman data set has at leastone of the following associated therewith: a corresponding test Ramanimage; and a corresponding test non-Raman image.

In still yet another embodiment, the test Raman image is used toidentify one or more regions of interest of the test renal sample,wherein the one or more regions of interest contain at least one of thefollowing: an epithelium tissue, a stroma tissue, and a nuclei tissue ofsaid test renal sample. From the one or more regions of interest, aplurality of test Raman spectra are obtained for the test renal sample.

In one embodiment, the chemometric technique is at least one of thefollowing: Principal Component Analysis, Minimum noise function,spectral mixture resolution, and linear discriminant analysis. In onesuch embodiment, the chemometric technique is Principal Componentanalysis in which the analysis is performed by selecting apre-determined vector space that mathematically describes the pluralityof reference Raman data sets. The test Raman data set is transformedinto the pre-determined vector space. A distribution of transformed datain the pre-determined vector space is analyzed so to generate the renaldisease state diagnosis.

In one embodiment, the analysis of the transformed data distribution isperformed by using a classification scheme. The classification schemeincludes at least one of the following: Mahalanobis distance, Adaptivesubspace detector, Band target entropy method, Neural network, andsupport vector machine. When the classification scheme is Mahalanobisdistance, a Mahalanobis distance is calculated between the test Ramandata set transformed into the pre-determined vector space and theplurality of reference Raman data sets in the pre-determined vectorspace so to generate the renal disease state diagnosis.

The present disclosure further provides for a system to provide adiagnosis of the renal disease state of a test renal sample. The systemincludes a reference database, an illumination source, a spectroscopicdevice, a machine readable program code and a processor. The referencedatabase contains a plurality of reference Raman data sets, eachreference Raman data set has an associated known renal sample and anassociated known renal disease state. The illumination source isconfigured to illuminate a test renal sample with substantiallymonochromatic light to generate scattered photons. The spectroscopicdevice is configured to collect a test Raman data set based on thescattered photons. The machine readable program code contains executableprogram instructions. The processor is operatively coupled to theillumination source and the spectroscopic device, and configured toexecute the machine readable program code so to perform a series ofsteps. In one embodiment, the spectroscopic device includes an imagingspectrometer. In another embodiment, the spectroscopic device includes adispersive spectrometer and a fiber array spectral translator.

The present disclosure further provides for a storage medium containingmachine readable program code, which, when executed by a processor,causes the processor to perform a series of steps. An irradiation sourceis configured to irradiate a test renal sample with substantiallymonochromatic light to thereby generate scattered photons. Aspectroscopic device is configured to collect a test Raman data setbased on the scattered photons. The test Raman data set is compared to aplurality of reference Raman data sets using a chemometric technique.Based on the comparison, a renal disease state of the test renal sampleis diagnosed.

The present disclosure further provides for a method to generate adiagnosis of renal disease state where a test Raman data set from a testrenal sample is generated at a data generation site remote from ananalysis center. The test Raman data set is transmitted over a datacommunication network to an analysis center. A database is provided atthe analysis center where the database contains a plurality of referenceRaman data sets, each reference Raman data set has an associated knownrenal sample and an associated known renal disease state. The test Ramandata set is compared to the plurality of reference Raman data sets atthe analysis center using a chemometric technique. Based on thecomparison, a renal disease state of the test renal sample is diagnosed.The diagnosis is transmitted to the data generation site via the datacommunication network.

The present disclosure further yet provides for a system to generate adiagnosis of renal disease state of a test renal sample. The systemincludes a data generation site, a communication interface, an analysissite, a database, a machine readable program code, and a processor. Thedata generation site has one or more spectroscopic devices whichgenerate a test Raman data set for a test renal sample. Thecommunication interface links the data generation site to a dataanalysis site. The database at the analysis site contains a plurality ofreference Raman data sets, each reference Raman data set has anassociated known renal sample and an associated known renal diseasestate. The machine readable program code, at the data analysis site,contains executable program instructions. The processor, at the dataanalysis site, is operatively coupled to the communication interface,and is configured to execute the machine readable program code toperform a series of steps including: facilitate transfer of the testRaman data set from the data generation site to the data analysis sitevia the communication interface; compare the test Raman data set to theplurality of reference Raman data sets using a chemometric technique;based on said comparison, diagnose a renal disease state of the testrenal sample; and transfer the diagnosis to the data generation site viathe data communication network. In one such embodiment, thespectroscopic device includes an imaging spectrometer. In another suchembodiment, the spectroscopic device includes a dispersive spectrometerand a fiber array spectral translator.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide furtherunderstanding of the disclosure and are incorporated in and constitute apart of this specification, illustrate embodiments of the disclosureand, together with the description, serve to explain the principles ofthe disclosure.

In the drawings:

FIG. 1 schematically represents an exemplary system of the presentdisclosure;

FIGS. 2A and 2B schematically represent an exemplary spectroscopy moduleof the present disclosure;

FIG. 3 schematically represents an exemplary system of the presentdisclosure;

FIGS. 4A-4C illustrate a Raman data set of one embodiment;

FIG. 5 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 6 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 7 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 8 illustrates a Raman spectrum and non-Raman images of a Gleason 6prostate cancer sample;

FIG. 9 illustrates regions of interest within a Raman image and theassociated Raman spectra;

FIG. 10 illustrates an exemplary classification model for Gleason 6prostate cancer samples;

FIG. 11 illustrates mean Raman spectra for progressive Gleason 6prostate cancer samples and non-progressive Gleason 6 prostate cancersamples;

FIG. 12 illustrates mean Raman spectra for progressive Gleason 7prostate cancer samples and non-progressive Gleason 6 prostate cancersamples;

FIG. 13 illustrates an exemplary classification model for Gleason 7prostate cancer samples;

FIG. 14 illustrates various non-Raman images of Gleason 7 prostatecancer samples:

FIG. 15 illustrates an exemplary classification model for Gleason 7prostate cancer samples;

FIG. 16 illustrates mean Raman spectra obtained for epithelium tissuefor Gleason 7 prostate cancer samples;

FIG. 17 illustrates reference Raman spectra for red blood cells, stroma,progressive Gleason 7 prostate cancer samples and non-progressiveGleason 7 prostate cancer samples;

FIG. 18 illustrates a Raman image montage for a Gleason 7 prostatesample;

FIG. 19 illustrates an image montage, obtained by spectral mixtureresolution, for a Gleason 7 prostate sample showing areas of stromatissue;

FIG. 20 illustrates an image montage, obtained by spectral mixtureresolution, for a Gleason 7 prostate sample showing areas of epitheliumtissue of a non-progressive Gleason 7 prostate cancer sample;

FIG. 21 illustrates an image montage, obtained by spectral mixtureresolution, for a Gleason 7 prostate sample showing areas of epitheliumtissue of a progressive Gleason 7 prostate cancer sample;

FIG. 22 illustrates an image montage, obtained by spectral mixtureresolution, for the empty slide areas of a Gleason 7 prostate sample;

FIG. 23 illustrates an image montage, obtained by spectral mixtureresolution, for a Gleason 7 prostate sample showing areas of red bloodcells of a progressive Gleason 7 prostate cancer sample;

FIG. 24 illustrates a color enhanced Raman molecular image of aprogressive Gleason 7 prostate cancer sample;

FIG. 25 illustrates several images of a Gleason 7 prostate cancersample;

FIG. 26 illustrates several non-Raman images and Raman spectra of kidneytissue;

FIG. 27 illustrates several non-Raman images and Raman spectra of breasttissue;

FIG. 28 illustrates several non-Raman images and Raman spectra of lungtissue;

FIG. 29 illustrates several non-Raman images and Raman spectra of braintissue;

FIG. 30 illustrates reference Raman spectra for samples identified ashaving a renal oncocytoma disease state and a chromophobe renalcarcinoma disease state;

FIG. 31 illustrates an exemplary classification model for renal tissuesamples;

FIG. 32 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 33 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 34 is a flow chart illustrating an exemplary method of the presentdisclosure;

FIG. 35 illustrates bright field images of tissue samples identified ashaving a renal oncocytoma disease state and a chromophobe renal cellcarcinoma disease state;

FIG. 36 illustrates a bright field image, a Raman image and a Ramanspectrum of a renal oncocytoma sample; and

FIG. 37 illustrates the application of the classification model of FIG.31 to four unknown renal tissue samples.

DETAILED DESCRIPTION OF THE DISCLOSURE

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the drawings to refer to the same or like parts.

Raman spectroscopy has utility in differentiating normal vs. malignanttissue and differentiating normal vs. benign tissue. In the case ofbreast cancer, the Raman spectra of malignant and benign tissues show anincrease in protein content and a decrease in lipid content versusnormal breast tissue. These results demonstrate that cancer diseasestates have a molecular basis for their origin. This method is notlimited to the prediction of the clinical outcome of cancer as themolecular basis for other disease states can also be detected by Ramanspectroscopy. Despite the reported ability to differentiate tissuetypes, there are no reports describing the use of Raman spectroscopy todiagnosis a renal sample as having a renal oncocytoma (“OC”) diseasestate or a chromophobe renal cell carcinoma (“ChRCC”) disease state.

The systems and methods described herein can potentially be utilized bya decision maker, such as pathologist, to identify type of kidneydisease in cases where existing lesions have overlapping histopathologicfeatures of OC and ChRCC. Because these diseases have differentprognoses and treatments, correctly identifying them has majorimplications for the health of patients.

FIG. 1 illustrates an exemplary system 100 according to one embodimentof the present disclosure. System 100 includes a spectroscopy module 110in communication with a processing module 120. Processing module 120 mayinclude a processor 122, databases 123, 124, 125 and 126, and machinereadable program code 128. The machine readable program code 128 maycontain executable program instructions, and the processor 122 may beconfigured to execute the machine readable program code 128 so as toperform the methods of the present disclosure. In one embodiment, theprogram code 128 may contain the ChemImage Xpert™ software marketed byChemImage Corporation of Pittsburgh, Pa. The Xpert™ software may be usedto process spectroscopic data and information received from thespectroscopy module 110 to obtain various spectral plots and images, andto also carry out various multivariate image analysis methods discussedlater herein below.

FIG. 2A illustrates an exemplary schematic layout of the spectroscopymodule 110 shown in FIG. 1. The layout in FIG. 2A may relate to theFalcon II™ Raman chemical imaging system marketed by ChemImageCorporation of Pittsburgh, Pa. In one embodiment, the spectroscopymodule 110 may include a microscope module 140 containing optics formicroscope applications. An illumination source 142 (e.g., a laserillumination source) may provide illuminating photons to a sample (notshown) handled by a sample positioning unit 144 via the microscopemodule 140. In one embodiment, photons transmitted, reflected, emitted,or scattered from the illuminated sample (not shown) may pass throughthe microscope module (as illustrated by exemplary blocks 146, 148 inFIG. 2A) before being directed to one or more of spectroscopy or imagingoptics in the spectroscopy module 110. In the embodiment of FIG. 2A,dispersive Raman spectroscopy 156, widefield Raman imaging 150, andbrightfield video imaging 152 are illustrated as “standard” operationalmodes of the spectroscopy module 110. Two optional imagingmodes—fluorescence imaging 154 and NIR (Near Infrared) imaging 158—mayalso be provided if desired. The spectroscopy module 110 may alsoinclude a control unit 160 to control operational aspects (e.g.,focusing, sample placement, laser beam transmission, etc.) of varioussystem components including, for example, the microscope module 140 andthe sample positioning unit 144 as illustrated in FIG. 2A. In oneembodiment, operation of various components (including the control unit160) in the spectroscopy module 110 may be fully automated or partiallyautomated, under user control.

It is noted here that in the discussion herein the terms “illumination,”“illuminating,” “irradiation,” and “excitation” are used interchangeablyas can be evident from the context. For example, the terms “illuminationsource,” “light source,” and “excitation source” are usedinterchangeably. Similarly, the terms “illuminating photons” and“excitation photons” are also used interchangeably. Furthermore,although the discussion hereinbelow focuses more on Raman spectroscopyand Raman molecular imaging, various methodologies discussed herein maybe adapted to be used in conjunction with other types of spectroscopyapplications as can be evident to one skilled in the art based on thediscussion provided herein.

FIG. 2B illustrates exemplary details of the spectroscopy module 110 inFIG. 2A according to one embodiment of the present disclosure.Spectroscopy module 110 may operate in several experimental modes ofoperation including bright field reflectance and transmission imaging,polarized light imaging, differential interference contrast (DIC)imaging, UV induced autofluorescence imaging, NIR imaging, wide fieldillumination whole field Raman spectroscopy, wide field spectralfluorescence imaging, and wide field spectral Raman imaging. Module 110may include collection optics 203, light sources 202 and 204, and aplurality of spectral information processing devices including, forexample: a tunable fluorescence filter 222, a tunable Raman filter 218,a dispersive spectrometer 214, a plurality of detectors including afluorescence detector 224, and Raman detectors 216 and 220, a fiberarray spectral translator (“FAST”) device 212, filters 208 and 210, anda polarized beam splitter (PBS) 219. In one embodiment, the processor122 (FIG. 1) may be operatively coupled to light sources 202 and 204,and the plurality of spectral information processing devices 214, 218and 222. In another embodiment, the processor 122 (FIG. 1), whensuitably programmed, can configure various functional parts of thespectroscopy module in FIG. 1 and may also control their operation atrun time. The processor, when suitably programmed, may also facilitatevarious remote data transfer and analysis operations discussed inconjunction with FIG. 3. Module 10 may optionally include a video camera205 for video imaging applications. Although not shown in FIG. 2B,spectroscopy module 110 may include many additional optical andelectrical components to carry out various spectroscopy and imagingapplications supported thereby.

A sample 201 may be placed at a focusing location (e.g., by using thesample positioning unit 144 in FIG. 2A) to receive illuminating photonsand to also provide reflected, emitted, scattered, or transmittedphotons from the sample 201 to the collection optics 203. Sample 201 mayinclude a variety of biological samples. In one embodiment, the sample201 includes at least one cell or a tissue containing a plurality ofcells. The sample may contain normal (non-diseased or benign) cells,diseased cells (e.g., cancerous tissues with or without a progressivecancer state or malignant cells with or without a progressive cancerstate) or a combination of normal and diseased cells. In one embodiment,the cell/tissue is a mammalian cell/tissue. Some examples of biologicalsamples may include prostate cells, kidney cells, lung cells, coloncells, bone marrow cells, brain cells, red blood cells, and cardiacmuscle cells. In one embodiment, the biological sample may includeprostate cells. In one such embodiment, the biological sample mayinclude Gleason 6 prostate cells. In another such embodiment, thebiological sample may include Gleason 7 prostate cells. In anotherembodiment the biological sample may include a renal sample. In one suchembodiment, the biological sample may include renal oncocytoma cells. Inanother such embodiment, the biological sample may include chromophoberenal carcinoma. In another embodiment, the sample 201 may include cellsof plants, non-mammalian animals, fungi protists, and monera. In yetanother embodiment, the sample 201 may include a test sample (e.g., abiological sample under test to determine its metabolic state or itsdisease status or to determine whether it is cancerous state wouldprogress to the next level). The “test sample,” “target sample” orunknown sample are used interchangeably herein to refer to a biologicalsample or renal sample under investigation, wherein such interchange usemay be without reference to such biological sample's metabolic state ordisease status.

A progressive cancer state is a cancer that will go on to becomeaggressive and acquire subsequent treatment by more aggressive means inorder for the patient to survive. An example of progressive cancer is aGleason score 7 cancer found in a prostate which has been surgicallyremoved, where the patient, subsequent to the removal of the prostate,develops metastatic cancer. In this example the cancer progressed evenafter the removal of the source organ. Progressive cancers can bedetected and identified in other organs and different types of cancer.

A non-progressive cancer is a cancer that does not progress to moreadvanced disease, requiring aggressive treatment. Many prostate cancersare non-progressive by this definition because though they are cancer bystandard histopathological definition, they do not impact the life ofthe patient in a way that requires significant treatment. In many casessuch cancers are observed and treated only if they show evidence ofbecoming progressive. Again, this is not a state particular to prostatecancer. Cancer cells are present in tissues of many health people.Because these do not ever transition to a state where they becomeprogressive in terms of growth, danger to the patient, or inconvenienceto the patient they would be considered non-progressive as the term isused herein.

The designation of progressive vs. non progressive can also be extendedto other disease or metabolic states. As an example, diabetes can beclinically described as “stable”, “well managed” by a clinician andwould fall into the non-progressive class. In contrast diabetes can beprogressing through the common course of the disease with all of theeffects on kidneys, skin, nerves, heart and other organs which are partof the disease. As a second example multiple sclerosis is a diseasewhich exists in many people is a stable, non-progressive state. In somepeople the disease rapidly progresses through historically observedpattern of physical characteristics with clinical manifestations.

The cells can be isolated cells, such as individual blood cells or cellsof a solid tissue that have been separated from other cells of thetissue (e.g., by degradation of the intracellular matrix). The cells canalso be cells present in a mass, such as a bacterial colony grown on asemi-solid medium or an intact or physically disrupted tissue. By way ofexample, blood drawn from a human can be smeared on the surface of asuitable Raman scattering substrate (e.g. an aluminum-coated glassslide) and individual cells in the sample can be separately imaged bylight microscopy and Raman scattering analysis using the spectroscopymodule 10 of FIG. 2B. Similarly a slice of a solid tissue (e.g., a pieceof fresh tissue or a paraffin-embedded thin section of a tissue) can beimaged on a suitable surface.

The cells can be cells obtained from a subject (e.g., cells obtainedfrom a human blood or urine sample, semen sample, tissue biopsy, orsurgical procedure). Cells can also be studied where they naturallyoccur, such as cells in an accessible location (e.g., a location on orwithin a human body), cells in a remote location using a suitable probe,or by revealing cells (e.g., surgically) that are not normallyaccessible.

Referring again to FIG. 2B, light source 202 may be used to irradiatethe sample 201 with substantially monochromatic light. Light source 202can include any conventional photon source, including, for example, alaser, an LED (light emitting diode), or other IR (infrared) or near IR(NIR) devices. The substantially monochromatic radiation reaching sample201 illuminates the sample 201, and may produce photons scattered fromdifferent locations on or within the illuminated sample 201. A portionof the Raman scattered photons from the sample 201 may be collected bythe collection optics 203 and directed to dispersive spectrometer 214 orRaman tunable filter 218 for further processing discussed later hereinbelow. In one embodiment, light source 202 includes a laser light sourceproducing light at 532.1 nm. The laser excitation signal is focused onthe sample 201 through combined operation of reflecting mirrors M1, M2,M3, the filter 208, and the collection optics 203 as illustrated by anexemplary optical path in the embodiment of FIG. 2B. The filter 208 maybe tilted at a specific angle from the vertical (e.g., at 6.5°) toreflect laser illumination onto the mirror M3, but not to reflectRaman-scattered photons received from the sample 201. The other filter210 may not be tilted (i.e., it remains at 0° from the vertical):Filters 208 and 210 may function as laser line rejection filters toreject light at the wavelength of laser light source 202.

In the spectroscopy module 110 in the embodiment of FIG. 2B, the secondlight source 204 may be used to irradiate the sample 201 withultraviolet light or visible light. In one embodiment, the light source204 includes a mercury arc (Hg arc) lamp that produces ultravioletradiation (UV) having wavelength at 365 nm form fluorescencespectroscopy applications. In yet another embodiment, the light source204 may produce visible light at 546 nm for visible light imagingapplications. A polarizer or neutral density (ND) filter with or withouta beam splitter (BS) may be provided in front of the light source 204 toobtain desired illumination light intensity and polarization.

In the embodiment of FIG. 2B, the dispersive spectrometer 214 and theRaman tunable filter 218 function to produce Raman data sets of sample201. A Raman data set corresponds to one or more of the following: aplurality of Raman spectra of the sample; and a plurality of spatiallyaccurate wavelength resolved Raman images of the sample. In oneembodiment, the plurality of Raman spectra is generated by dispersivespectral measurements of individual cells. In this embodiment, theillumination of the individual cell may cover the entire area of thecell so the dispersive Raman spectrum is an integrated measure ofspectral response from all the locations within the cell.

In another embodiment, the Raman data set corresponds to a threedimensional block of Raman data (e.g., a spectral hypercube or a Ramanimage) having spatial dimensional data represented in the x and ydimensions and wavelength data represented in the z dimension asexemplified in FIGS. 4A-4C. Each Raman image has a plurality of pixelswhere each has a corresponding x and y position in the Raman image. TheRaman image may have one or more regions of interest. The regions ofinterest may be identified by the size and shape of one or more pixelsand is selected where the pixels are located within the regions ofinterest. A single Raman spectrum is then extracted from each pixellocated in the region of interest, leading to a plurality of Ramanspectra for each of the regions of interest. The extracted plurality ofRaman spectra are then designated as the Raman data set. In thisembodiment, the plurality of Raman spectra and the plurality ofspatially accurate wavelength resolved Raman images are generated, ascomponents of the hypercube, by a combination of the Raman tunablefilter 218 and Raman imaging detector 220 or by a combination of theFAST device 212, the dispersive spectrometer 214, and the Raman detector216.

In yet another embodiment, a Raman dataset is generated using a Ramanimage to identify one or more regions of interest of the sample 201. Inone such embodiment, the one or more regions of interest contain atleast one of the following: an epithelium area, a stroma area,epithelial-stromal junction (ESJ) area and/or nuclei area. A pluralityof Raman spectra may be obtained from the one or more of regions ofinterest of the sample 201. In standard operation the Raman spectrumgenerated by selecting a region of interest in a Raman image is theaverage spectrum of all the spectra at each pixel within the region ofinterest. The standard deviation between of all the spectra in theregion of interest may be displayed along with the average Ramanspectrum of the region of interest. Alternatively, all of the spectraassociated with pixels within a region can be considered as a pluralityof spectra, without the step of reducing them to a mean and standarddeviation.

With further reference to FIG. 2B, the fluorescence tunable filter 222may function to produce fluorescence data sets of the photons emittedfrom the sample 201 under suitable illumination (e.g., UV illumination).In one embodiment, the fluorescence data set includes a plurality offluorescence spectra of sample 201 and/or a plurality of spatiallyaccurate wavelength resolved fluorescence images of sample 201. Afluorescence spectrum of sample 210 may contain a fluorescence emissionsignature of the sample 201. In one embodiment, the emission signaturemay be indicative of a fluorescent probe (e.g., fluoresceinisothiocyanate) within the sample 201. The fluorescence data sets may bedetected by fluorescence CCD detector 224. A portion of the fluorescenceemitted photons or visible light reflected photons from the sample 201may be directed to the video imaging camera 205 via a mirror M4 andappropriate optical signal focusing mechanism.

In one embodiment, a microscope objective (including the collectionoptics 203) may be automatically or manually zoomed in or out to obtainproper focusing of the sample.

The entrance slit (not shown) of the spectrometer 214 may be opticallycoupled to the output end of the fiber array spectral translator device212 to disperse the Raman scattered photons received from the FASTdevice 212 and to generate a plurality of spatially resolved Ramanspectra from the wavelength-dispersed photons. The FAST device 212 mayreceive Raman scattered photons from the beam splitter 219, which maysplit and appropriately polarize the Raman scattered photons receivedfrom the sample 201 and transmit corresponding portions to the input endof the FAST device 212 and the input end of the Raman tunable filter218.

Referring again to FIG. 2B, the tunable fluorescence filter 222 and thetunable Raman filter 218 may be used to individually tune specificphoton wavelengths of interest and to thereby generate a plurality ofspatially accurate wavelength resolved spectroscopic fluorescence imagesand Raman images, respectively, in conjunction with correspondingdetectors 224 and 220. In one embodiment, each of the fluorescencefilter 222 and the Raman filter 218 includes a two-dimensional tunablefilter, such as, for example, an electro-optical tunable filter, aliquid crystal tunable filter (LCTF), or an acousto-optical tunablefilter (AOTF). A tunable filter may be a band-pass or narrow band filterthat can sequentially pass or “tune” fluorescence emitted photons orRaman scattered photons into a plurality of predetermined wavelengthbands. The plurality of predetermined wavelength bands may includespecific wavelengths or ranges of wavelengths. In one embodiment, thepredetermined wavelength bands may include wavelengths characteristic ofthe sample undergoing analysis. The wavelengths that can be passedthrough the fluorescence filter 222 and Raman filter 218 may range from200 mm (ultraviolet) to 2000 nm (i.e., the far infrared). The choice ofa tunable filter depends on the desired optical region and/or the natureof the sample being analyzed. Additional examples of a two-dimensionaltunable filter may include a Fabry Perot angle tuned filter, a Lyotfilter, an Evans split element liquid crystal tunable filter, a Solcliquid crystal tunable filter, a spectral diversity filter, a photoniccrystal filter, a fixed wavelength Fabry Perot tunable filter, anair-tuned Fabry Perot tunable filter, a mechanically-tuned Fabry Perottunable filter, and a liquid crystal Fabry Perot tunable filter. Asnoted before, the tunable filters 218, 222 may be selected to operate inone or more of the following spectral ranges: the ultraviolet (UV),visible, and near infrared. In one such embodiment, the tunable filters218, 222 may be selected to operate in spectra ranges of 900-1155 cm⁻¹and 15-30-1850 cm⁻¹ Raman shift values.

In one embodiment, a multi-conjugate filter (MCF) may be used instead ofa simple LCTF (e.g., the LCTF 218 or 222) to provide more precisewavelength tuning of photons received from the sample 201. Someexemplary multi-conjugate filters are discussed, for example, in U.S.Pat. No. 6,992,809, titled “Multi-Conjugate Liquid Crystal TunableFilter;” and in the United States Published Patent Application NumberUS2007/0070260A1, titled “Liquid Crystal Filter with Tunable RejectionBand,” the disclosures of both of these publications are incorporatedherein by reference in their entireties.

In the embodiment of FIG. 2B, the fluorescence spectral data sets(output from the tunable filter 222) may be detected by the detector224, and the Raman spectral data sets (output from the spectrometer 214and the tunable filter 218) may be detected by detectors 216 and 220.The detectors 216, 220, and 224 may detect received photons in aspatially accurate manner. Detectors 216, 220 and 224 may include anoptical signal (or photon) collection device such as, for example, animage focal plane array (FPA) detector, a charge coupled device (CCD)detector, or a CMOS (Complementary Metal Oxide Semiconductor) arraysensor. Detectors 216, 220 and 224 may measure the intensity ofscattered, transmitted or reflected light incident upon their sensingsurfaces (not shown) at multiple discrete locations or pixels, andtransfer the spectral information received to the processor module 120for storage and analysis. The optical region employed to characterizethe sample of interest governs the choice of two-dimensional arraydetector. For example, a two-dimensional array of silicon charge-coupleddevice (CCD) detection elements can be employed with visible wavelengthemitted or reflected photons, or with Raman scatter photons, whilegallium arsenide (GaAs) and gallium indium arsenide (GaInAs) PPAdetectors can be employed for image analyses at near infraredwavelengths. The choice of such devices may also depend on the type ofsample being analyzed.

In one embodiment, a display unit (not shown) may be provided to displayspectral data collected by various detectors 216, 220, 224 in apredefined or user-selected format. The display unit may be a computerdisplay screen, a display monitor, an LCD (liquid crystal display)screen, or any other type of electronic display device.

Referring again to FIG. 1, the databases 123-126 may store variousreference spectral data sets including, for example, a reference Ramandata set, a reference fluorescence data set, a reference NIR data set,etc. The reference data sets may be collected from different samples andmay be used to detect or identify the sample 201 from comparison of itsspectral data set with the reference data sets. In one embodiment,during operation, the Raman data sets and fluorescence data sets of thesample 201 also may be stored in one or more of the databases (e.g.,database 123) of the processing module 120.

For example, in one embodiment, database 123 may be used to store aplurality of reference Raman data sets from reference cells having aknown metabolic state or a known disease state. In one such embodiment,the reference Raman data sets may correspond to a plurality of referenceRaman spectra. FIG. 30 illustrates two exemplary reference Raman spectrafor known samples being diagnosed as having a renal oncocytoma diseasestate and a chromophobe renal carcinoma disease state, based onhistopathological examination. Spectrum 3010 corresponds to the Ramanspectrum for a sample diagnosed as having renal oncocytoma disease.Spectrum 3020 corresponds to the Raman spectrum for a sample diagnosedas having chromophobe renal carcinoma. In another such embodiment, thereference Raman data sets may correspond to a plurality of referencespatially accurate wavelength resolved Raman images.

In another embodiment, the database 124 may be used to store a firstplurality of reference Raman data sets from reference normal(non-diseased) cells. In one such embodiment, the reference normal cellscorrespond to renal oncocytoma cells. In one embodiment, the firstreference set of Raman data sets may include a plurality of firstreference Raman spectra. In another embodiment, a first reference Ramanspectrum may correspond to a dispersive Raman spectrum. In a furtherembodiment, the first reference set of Raman data sets may include aplurality of first reference spatially accurate wavelength resolvedRaman images obtained from corresponding reference normal cells.

In yet another embodiment, the database 125 may store a second pluralityof reference Raman data sets from different types of reference diseasedcells. In one such embodiment, the reference diseased cells correspondto chromophobe renal carcinoma cells. In one embodiment, the secondreference set of Raman data sets includes a plurality of secondreference Raman spectra. In one embodiment, the second reference Ramanspectrum may correspond to a dispersive Raman spectrum. In anotherembodiment, the second reference set of Raman data sets may include aplurality of second reference spatially accurate wavelength resolvedRaman images obtained from corresponding reference diseased cells.

Similarly, database 126 may store a plurality of reference fluorescencespectra and/or a plurality of reference spatially accurate wavelengthresolved fluorescence spectroscopic images obtained from referencebiological samples (e.g., cancerous human tissues). One or more of thereference biological samples may include fluorescence probe molecules(e.g., fluorescein isothiocyanate). In one embodiment, a single databasemay be used to store all types of spectra.

The reference Raman data sets may be associated with a reference Ramanimage and/or a corresponding reference non-Raman image. In one suchembodiment, the reference non-Raman image may include at least one of: abrightfield image; a polarized light image; and a UV-inducedautofluorescence image.

FIG. 3 depicts an exemplary setup to remotely perform spectroscopicanalysis of test samples according to one embodiment of the presentdisclosure. Spectroscopic data from a test sample or a test sample maybe collected at a data generation site 260 using a spectroscopy module265. In one embodiment, the spectroscopy module may be functionallysimilar to the spectroscopy module 110 discussed hereinbefore withreference to FIGS. 2A-2B. The spectroscopic data collected at the datageneration site 260 may be transferred to a data analysis site 270 via acommunication network 272. In one embodiment, the communication network272 may be any data communication network such as an Ethernet LAN (localarea network) connecting all the data processing aid computing unitswithin a facility, e.g., a university research laboratory, or acorporate research center. In that case, the data generation site 260and the data analysis site 270 may be physically located within the samefacility, e.g., a university research laboratory or a corporate researchcenter. In alternative embodiments, the communication network 272 mayinclude, independently or in combination, any of the present or futurewireline or wireless data communication networks such as, for example,the Internet, the PSTN (public switched telephone network), a cellulartelephone network, a WAN (wide area network), a satellite-basedcommunication link, a MAN (metropolitan area network), etc. In thiscase, the data generation site 260 and the data analysis site 270 may bephysically located in different facilities. In some embodiments, thedata generation site 260 and the data analysis site 270 that are linkedby the communication network 272 may be owned or operated by differententities.

The data analysis site 270 may include a processing module 275 toprocess the spectroscopic data received from the data generation site260. In one embodiment, the processing module 275 may be similar to theprocessing module 120 and may also include a number of differentdatabases (not shown) storing different reference spectroscopic datasets (e.g., a first plurality of reference Raman data sets fornon-progressive cancer tissues, a second plurality of reference Ramandata sets for progressive cancer tissues, a third plurality of referenceRaman data sets for normal or non-diseased tissues, a fourth pluralityof reference data set for renal oncocytomas samples and chromophoberenal cell carcinoma samples, etc.). The processing module 275 mayinclude a processor (similar to the processor 122 of the processingmodule 120 in FIG. 1) that is configured to execute program code orsoftware to perform various spectral data processing tasks according tothe teachings of the present disclosure. The machine-readable programcode containing executable program instructions may be initially storedon a portable data storage medium, e.g., a floppy diskette 294, acompact disc or a DVD 295, a data cartridge tape (not shown), or anyother suitable digital data storage medium. The processing module 275may include appropriate disk drives to receive the portable data storagemedium and may be configured to read the program code stored thereon,thereby facilitating execution of the program code by its processor. Theprogram code, upon execution by the processor of the processing module275, may cause the processor to perform a variety of data processing anddisplay tasks including, for example, initiate transfer of spectral dataset from the data generation site 260 to the data analysis site 270 viathe communication network 272, compare the received spectral data set tovarious reference data sets stored in the databases of the processingmodule 275, classify or identify the test sample based on the comparison(e.g., whether the test sample has a progressive cancer ornon-progressive cancer state or whether the test sample has renaloncocytomas disease or chromophobe renal cell carcinoma disease),transfer the classification or identification results to the datageneration site 260 via the communication network 272, etc.

In one embodiment, the data analysis site 270 may include one or morecomputer terminals 286A-286C communicatively connected to the processingmodule 275 via corresponding data communication links 290A-290C, whichcan be serial, parallel, or wireless communication links, or a suitablecombination thereof. Thus, users may utilize functionalities of theprocessing module 275 via their computer terminals 286A-286C, which mayalso be used to display spectroscopic data received from the datageneration site 260 and the results of the spectroscopic data processingby the processing module 275, among other applications. It is evidentthat in a practical application, there may be many more computerterminals 286 than just three terminals shown in FIG. 3.

The computer terminals 286A-286C may be, e.g., a personal computer (PC),a graphics workstation, a multiprocessor computer system, a distributednetwork of computers, or a computer chip embedded as part of a machineor mechanism. Similarly, the data generation site 260 may include one ormore of such computers (not shown) for viewing the results of thespectroscopic analysis received from the data analysis site 270. Eachcomputer terminal, whether at the data generation site 260 or at thedata analysis site 270, may include requisite data storage capability inthe form of one or more volatile and non-volatile memory modules. Thememory modules may include RAM (random access memory), ROM (read onlymemory) and HDD (hard disk drive) storage.

It is noted that the arrangement depicted in FIG. 3 may be used toprovide a commercial, network-based spectroscopic data processingservice that may perform customer-requested processing of spectroscopicdata in real time or near real time. For example, the processing module275 at the data analysis site 270 may be configured to identify a testsample from the spectroscopic data remotely submitted to it over thecommunication network 272 (e.g., the Internet) from the spectroscopymodule 265 automatically or through an operator at the data generationsite 260. The client site (data generation site) 260 may be, forexample, a government laboratory or a medical facility or pathologicallaboratory. The results of spectroscopic data analysis may betransmitted back to the client site 260 for review and further analysis.In one embodiment, the whole data submission, analysis, and reportingprocess can be automated.

It is further noted that the owner or operator of the data analysis site270 may commercially offer a network-based spectroscopic data contentanalysis service, as illustrated by the arrangement in FIG. 3, tovarious individuals, corporations, governmental entities, laboratories,or other facilities on a fixed-fee basis, on a per-operation basis or onany other payment plan mutually convenient to the service provider andthe service recipient.

Processing module 120 may also include a test Raman database associatedwith a test biological sample having an unknown metabolic state. In onesuch embodiment, the test Raman data set may correspond to a pluralityof Raman spectra of the test biological sample. In another suchembodiment, the test Raman data set may correspond to a plurality ofspatially accurate wavelength resolved Raman images of the testbiological sample. In another embodiment, each of the test Raman datasets may be associated with least one of the following: a correspondingtest Raman image; and a corresponding test non-Raman image. In one suchembodiment, the test non-Raman image may include at least one of thefollowing: a brightfield image; a polarized light image; and aUV-induced autofluorescence image.

In one such embodiment, processing module 120 may also include a testRaman database associated with a test renal sample having an unknownrenal disease state. In one such embodiment, the test Raman data set maycorrespond to a plurality of Raman spectra of the test renal sample. Inanother such embodiment, the test Raman data set may correspond to aplurality of spatially accurate wavelength resolved Raman images of thetest renal sample. In another embodiment, each of the test Raman datasets may be associated with least one of the following: a correspondingtest Raman image; and a corresponding test non-Raman image. In one suchembodiment, the test non-Raman image may include at least one of thefollowing: a brightfield image; a polarized light image; and aUV-induced autofluorescence image.

In one embodiment, the test Raman spectra are generated using a testRaman image to identify one or more regions of interest of the testbiological sample or the test renal sample. In one such embodiment, theone or more regions of interest contain at least one of the following:an epithelium area, a stroma area, epithelial-stromal junction (ESJ)area, and/or nuclei area. A plurality of test Raman spectra may beobtained from the one or more of regions of interest of the testbiological sample or the rest renal sample.

A diagnosis of a test sample as diseased or non-diseased or a predictionof the metabolic state of a test sample may be made by comparing a testRaman data set to reference Raman data sets using a chemometrictechnique. In one such embodiment, a diagnosis of a test renal sample ashaving a renal oncocytoma disease state or a chromophobe renal carcinomadisease state is generated. The chemometric technique may include atleast one of the following: Principal Component Analysis, Minimum noisefraction, spectral mixture resolution and linear discriminant analysis.

In one embodiment, the chemometric technique may be spectral unmixing.The application of spectral unmixing to determine the identity ofcomponents of a mixture is described in U.S. Pat. No. 7,072,770,entitled “Method for Identifying Components of a Mixture via SpectralAnalysis, issued on Jul. 4, 2006, which is incorporated herein byreference in it entirety. Spectral unmixing as described in the abovereferenced patent can be applied as follows: Spectral unmixing requiresa library of spectra which include possible components of the testsample. The library can in principle be in the form of a single spectrumfor each component, a set of spectra for each component, a single Ramanimage for each component, a set of Raman images for each component, orany of the above as recorded after a dimension reduction procedure suchas Principle Component Analysis. In the methods discussed herein, thelibrary used as the basis for application of spectral unmixing is thereference Raman data sets.

With this as the library, a set of Raman measurements made on a sampleof unknown state, described herein as a test Raman data set, is assessedusing the methods of U.S. Pat. No. 7,072,770 to determine the mostlikely groups of components which are present in the sample. In thisinstance the components are actually disease states of interest and/orclinical outcome. The result is a set of disease state groups and/orclinical outcome groups with a ranking of which are most likely to berepresented by the test data set.

Given a set of reference spectra, such as those described above, a pieceor set of test data can be evaluated by a process called spectralmixture resolution. In this process, the test spectrum is approximatedwith a linear combination of reference spectra with a goal of minimizingthe deviation of the approximation from the test spectrum. This processresults in a set of relative weights for the reference spectra.

In one embodiment, the chemometric technique may be Principal ComponentAnalysis. Using Principal Component Analysis results in a set ofmathematical vectors defined based on established methods used inmultivariate analysis. The vectors form an orthogonal basis, meaningthat they are linearly independent vectors. The vectors are determinedbased on a set of input data by first choosing a vector which describesthe most variance within the input data. This first “principalcomponent” or PC is subtracted from each of the members of the inputset. The input set after this subtraction is then evaluated in the samefashion (a vector describing the most variance in this set is determinedand subtracted) to yield a second vector—the second principal component.The process is iterated until either a chosen number of linearlyindependent vectors (PCs) are determined, or a chosen amount of thevariance within the input data is accounted for.

In one embodiment, the Principal Component Analysis may include a seriesof steps. A pre-determined vector space is selected that mathematicallydescribes a plurality of reference Raman data sets. Each reference Ramandata set may be associated with a known biological sample having anassociated metabolic state. The test Raman data set may be transformedinto the pre-determined vector space, and then a distribution oftransformed data may be analyzed in the pre-determined vector space togenerate a diagnosis.

In another embodiment, the Principal Component Analysis may include aseries of steps. A pre-determined vector space is selected thatmathematically describes a first plurality of reference Raman data setsassociated with a known biological sample having an associated diseasedstate and a second plurality of reference Raman data sets associatedwith a known biological sample having an associated non-diseased state.The test Raman data set may be transformed into the pre-determinedvector space, and then a distribution of transformed data may beanalyzed in the pre-determined vector space to generate a diagnosis.

In yet another embodiment, the Principal Component Analysis may includea series of steps. A pre-determined vector space is selected thatmathematically describes a first plurality of reference Raman data setsassociated with a known biological sample having an associatedprogressive state and a second plurality of reference Raman data setsassociated with a known biological sample having an associatednon-progressive state. The test Raman data set may be transformed intothe pre-determined vector space, and then a distribution of transformeddata may be analyzed in the pre-determined vector space to generate adiagnosis.

In still yet another embodiment, the Principal Component Analysis mayinclude a series of steps. A pre-determined vector space is selectedthat mathematically describes a first plurality of reference Raman datasets associated with a known renal sample having an associated renaloncocytoma disease state and a second plurality of reference Raman datasets associated with a known renal sample having an associatedchromophobe renal carcinoma disease state. The test Raman data set maybe transformed into the pre-determined vector space, and then adistribution of transformed data may be analyzed in the pre-determinedvector space. FIG. 31 illustrates scatter plots generated by applyingPrincipal Component Analysis on such reference Raman data setsassociated with known renal samples. The scatter plots show a clearseparation between the reference data associated with the known renaloncocytoma samples and referenced data associated with the known renalcarcinoma disease samples in principal component space. Data points 3110correspond to data for the known renal oncocytoma samples and datapoints 3120 correspond to known chromophobe renal carcinoma diseasesamples.

The analysis of the distribution of the transformed data may beperformed using a classification scheme. Some examples of theclassification scheme may include: Mahalanobis distance, Adaptivesubspace detector, Band target entropy method, Neural network, andsupport vector machine as an incomplete list of classification schemesknown to those skilled in the art.

In one such embodiment, the classification scheme is Mahalanobisdistance. The Mahalanobis distance is an established measure of thedistance between two sets of points in a multidimensional space thattakes into account both the distance between the centers of two groups,but also the spread around each centroid. A Mahalanobis distance modelof the data is represented by plots of the distribution of the spectrain the principal component space. The Mahalanobis distance calculationis a general approach to calculating the distance between a single pointand a group of points. It is useful because rather than taking thesimple distance between the single point and the mean of the group ofpoints, Mahalanobis distance takes into account the distribution of thepoints in space as part of the distance calculation. The Mahalanobisdistance is calculated using the distances between the points in alldimensions of the principal component space.

In one such embodiment, once the test Raman data is transformed into thespace defined by the pre-determined PC vector space the test data isanalyzed relative to the pre-determined vector space. This may beperformed by calculating a Mahalanobis distance between the test Ramandata set transformed into the pre-determined vector space and the Ramandata sets in the pre-determined vector space to generate a diagnosis.

The exemplary systems of FIGS. 1 and 2 may be used to perform methods topredict the clinical outcome of patients or diagnose a disease state ofpatients. Processor 122 is configured to execute program instructions tocarry out these methods. One such embodiment is illustrated in FIG. 5which shows a flow chart for a method of the present disclosure. In step510, a Raman data set is obtained from a group of known biologicalsamples. Each Raman data set is analyzed to identify a diseased ornon-diseased reference Raman data set depending on whether therespective biological sample is a non-diseased sample or a diseasedsample, step 520. From the Raman data sets, a first database isgenerated where the first database contains data for all diseasedreference Raman data sets. A second database is also generated where thesecond database contains data for all non-diseased reference Raman datasets, step 530. In step 540, a test Raman data set of a test biologicalsample is received where the test sample has an unknown disease status.In step 550, a diagnosis of whether the test sample is a non-diseasedsample or a diseased sample is generated by comparing the test Ramandata set against the reference Raman data sets in the first and thesecond databases using a chemometric technique.

In another such embodiment, FIG. 6 illustrates a flow chart for anothermethod of the present disclosure. In step 610, a database is providedwhere the database contains a plurality of reference Raman data sets. Instep 620, a test biological sample is irradiated with substantiallymonochromatic light generating scattered photons. Based on the scatteredphotons, a test Raman data set is collected, in step 630. The test Ramandata set is compared to the plurality of reference Raman data sets usinga chemometric technique, in step 640. Based on the comparison, ametabolic state of the test biological sample is predicted, in step 650.

In another embodiment of the present disclosure, the exemplary system ofFIG. 3 may be used to carry out methods to predict the clinical outcomeof patients. In this method, data obtained at a data generation site istransmitted to an analysis site to obtain a prediction of the metabolicstate of a test biological sample. The prediction is then transmittedback to the data generation site. The transmission may be performed overa data communication network such as the Internet. FIG. 7 illustrates anexemplary flow chart for such a method. In step 710, a test Raman dataset of a test biological sample is obtained at a data generation site.The test Raman data set is transmitted over a data communication networkto an analysis center, in step 720. A database is provided at ananalysis center where the database contains a plurality of referenceRaman data sets, step 730. Each reference Raman data set has anassociated known biological sample and an associated known metabolicstate. The Raman data set is compared to the plurality of referenceRaman data sets at the analysis center using a chemometric technique, instep 740. Based on the outcome of this comparison, a metabolic state ofthe test biological sample is predicted, in step 750. The prediction isthen transmitted to the data generation site via the data communicationnetwork, in step 760.

Yet another embodiment is illustrated in FIG. 32 which shows a flowchart for a method of the present disclosure. In step 3210, a Raman dataset is obtained from a group of known renal samples. Each Raman data setis analyzed to identify an oncocytoma disease reference Raman data setor chromophobe renal carcinoma disease reference Raman data depending onwhether the respective renal sample is an oncocytoma disease sample orchromophobe renal carcinoma disease sample, step 3220. From the Ramandata sets, a first database is generated where the first databasecontains data for all oncocytoma disease reference Raman data sets. Asecond database is also generated where the second database containsdata for all chromophobe renal carcinoma reference Raman data sets, step3230. In step 3240, a test Raman data set of a test renal sample isreceived where the test sample has an unknown renal disease status. Instep 3250, a diagnosis of whether the test sample has a oncocytomadisease state or a chromophobe renal carcinoma disease state isgenerated by comparing the test Raman data set against the referenceRaman data sets in the first and the second databases using achemometric technique.

Still yet another such embodiment is illustrated in FIG. 33. In step3310, a database is provided where the database contains a plurality ofreference Raman data sets. In step 3320, a test renal sample isirradiated with substantially monochromatic light generating scatteredphotons. Based on the scattered photons, a test Raman data set iscollected, in step 3330. The test Raman data set is compared to theplurality of reference Raman data sets using a chemometric technique, instep 3340. Based on the comparison, a diagnosis of a renal disease stateis generated in step 3350. The diagnosis may include oncocytoma diseasestate or chromophobe renal carcinoma disease state.

In another embodiment of the present disclosure, the exemplary system ofFIG. 3 may be used to carry out methods to diagnose the renal diseasestates of patients. In this method, data obtained at a data generationsite is transmitted to an analysis site to obtain a diagnosis of therenal disease state of a test renal sample. The diagnosis is thentransmitted back to the data generation site. The transmission may beperformed over a data communication network such as the Internet. FIG.34 illustrates an exemplary flow chart for such a method. In step 3410,a test Raman data set of a test renal sample is obtained at a datageneration site. The test Raman data set is transmitted over a datacommunication network to an analysis center, in step 3420. A database isprovided at an analysis center where the database contains a pluralityof reference Raman data sets, step 3430. Each reference Raman data sethas an associated known renal sample and an associated known renaldisease state. The Raman data set is compared to the plurality ofreference Raman data sets at the analysis center using a chemometrictechnique, in step 3440. Based on the outcome of this comparison, adiagnosis of the renal disease state of the test biological sample isprovided, in step 750. The diagnosis is then transmitted to the datageneration site via the data communication network, in step 3460. Thediagnosis may include oncocytoma disease state or chromophobe renalcarcinoma disease state.

EXAMPLES

The following examples demonstrate the method and system of the presentdisclosure.

The samples discussed in the below examples were tissue samples preparedusing standard histology techniques from paraffin embedded tissuesections which reside in a clinical sample database. Five (5) micronthick sections were prepared and placed on an aluminum side of analuminum coated glass slide. Paraffin was removed using standardprocedures and solvents. An adjacent section was prepared in standardfashion and stained with hematoxalin and Eosin for routine pathologyanalysis. Expert pathologists reviewed each sample and confirmed thediagnosis.

Raman spectra, under widefield illumination conditions, were obtainedfor each of the twenty tissue areas using the Falcon II™ Raman imagingsystem from ChemImage Corporation of Pittsburgh, Pa. Typical Ramandispersive spectral were collected from cells using 595 W/cm² laserpower density, 100× objective, and appropriate exposure times to getgood signal to noise (typically 10-60 s). Baseline, dark current andbias corrections were applied to the acquired spectra. Spectralprocessing and data analysis was performed using ChemImage Xpert™ 2.0software available from ChemImage Corporation of Pittsburgh, Pa. Typicalspatially accurate wavelength resolved Raman chemical images wereacquired using 514 W/cm² laser power density, 50× objective, 8×8binning, and 1.5 s exposure time, and 5 averages over the spectral rangeof 600-3200 cm⁻¹. These parameters are typical for the data discussedbelow.

Example 1

The example demonstrates the creation of a reference Raman databasehaving progressive Raman data sets and non-progressive Raman data setsfor Gleason 6 cancer tissue. A series of case-control pairs of patientswere selected for analysis. A case sample was defined as a patient whodeveloped prostate cancer characterized as having a Gleason 6 patternand developed metastatic prostate cancer after removal of the prostate.For the purposes of this application, a case sample from a patient whodeveloped metastatic cancer is defined having progressive cancer. Acontrol sample was selected to match each case sample in terms ofrelative clinical variable but the patient did not develop metastaticprostate cancer after removal of the prostate. For the purposes of thisapplication, a control sample, having cancer but no development ofmetastasis, is defined having non-progressive cancer.

An unstained thin section of a tissue sample, for each case and controlsample, was placed on the stage of a FALCON II™ Raman imagingmicroscope. Twenty (20) tissue areas were evaluated on each unstainedtissue sample section. For each area, non-Raman images were acquiredusing multiple modalities including bright field reflectance, crosspolarized light reflectance, integrated auto fluorescence under UVexcitation, differential interference contrast, and monochromaticexcitation. After collection of Raman data sets (dispersive spectrumunder wide field illumination, Raman image), brightfield, crosspolarized light reflectance and autofluorescence images, the sample wasstained using standard pathology routines with haematoxylin and Eosin.Subsequent to staining a digital image of the stained sample wasacquired. These non-Raman images were obtained for the same field ofview using the procedures described in U.S. patent application Ser. No.11/647,195, filed Dec. 29, 2006, and entitled “Method for CorrelatingSpectroscopic Measurements with Digital Images of Contrast EnhancedImages,” which is incorporated by reference herein in its entirety.

FIG. 8 illustrates an exemplary data set for a tissue sample, includinga bright field image 820, a polarized light image 830, anautofluorescence image 840, a stained image 850 and a Raman spectrum 850for a selected tissue area.

For this tissue area, four regions of interest, 910, 915, 920 and 925were selected as illustrated in FIG. 9. A Raman spectrum was thenobtained for each region of interest where spectrum 930 is associatedwith region of interest 910, spectrum 935 is associated with region ofinterest 935, spectrum 920 is associated with region of interest 940,and spectrum 925 is associated with region of interest 945. Similardata, as shown in FIGS. 8 and 9, were obtained for each of the threecase-control pairs. Principal component analysis was applied to theRaman data sets for each case-control pair.

From the evaluation of the reference Raman spectra obtained for theprogressive prostate tissue and the non-progressive cancer tissue,scatter plots were generated showing a separation between the data forthe progressive and non-progressive cancer tissue samples in principalcomponent space. FIG. 10 represents an exemplary pre-determined vectorspace, or a projection of the vector space onto the two coordinates (PC3and PC4). As shown in FIG. 10, the points labeled 1010, 1015 and 1020mathematically describe the reference Raman spectra data sets collectedfor progressive tissue. The points labeled 1025, 1030 and 1035mathematically describe the reference Raman spectra data sets collectedfor non-progressive tissue. The scatter plots of the distribution of theRaman spectra in principal component space show a clear differencebetween progressive tissue 1010, 1015 and 1020 and non-progressivetissue 1025, 1030 and 1035. The data points associated with the Ramanspectra for non-progressive tissue are clustered in the same area of theprincipal component space and are separate from the data pointsassociated with the Raman spectra for progressive tissue.

Once the vector space is established, classification of a test Ramandataset is performed by transforming the test Raman dataset into thevector space and analyzing which group the transformed data lies nearestto. The determination of the metabolic state (in this example whetherthe cancer is going to be progressive) is made by selecting the groupwhich the test data set lies closest to after the transformation.

To demonstrate the feasibility of the methods of the present disclosure,each Raman spectrum, for the Gleason 6 tissue samples, was thenclassified as progressive tissue or non-progressive tissue by using aLeave-One-Out (LOO) cross validation approach wherein a classificationmodel (vector space) was generated with all of the Raman data setsexcept a single spectrum (test Raman data set). The classification modelthus generated was used to classify the one spectrum (test Raman dataset) which was left out. The process was repeated for all spectra.

The results of the LOO are shown below in Table 1. These results areconsistent with a sensitivity of 93% and a specificity of 93%.

TABLE 1 Gleason 7 Gleason 7 Sample Progressive Non-Progressive # ofsamples 58 60 Classified as progressive 54 4 Classified asnon-progressive 4 56

Mean Raman spectra were generated for each tissue type from the 40 Ramanspectra collected as described above. FIG. 11 shows mean Raman spectrum1120 from known tissue samples of patients who progressed to metastaticprostate disease 1120, and the mean Raman spectrum 1120 from tissuesamples of patients who did not progress to metastatic prostate disease.Subtle differences, in these mean spectra, are indicators of thepresence or absence of components which are responsible for theprogressive or non-progressive nature of the Gleason 6 prostate cancer.

From this example, several conclusions can be drawn. First, Ramanspectroscopy is capable of detecting the components of prostate tissuewhich are responsible for the progressive or non-progressive nature ofthe cancer. Second, well characterized Gleason 6 prostate cancer tissuesamples may be used to generate reference Raman data sets from which aclassification mode, based on principal component analysis, may begenerated. Using this classification model, the progressive ornon-progressive nature of a prostate cancer sample can be predicted.

Example 2

The example demonstrates the creation of a reference Raman database forreference progressive Raman data sets and reference non-progressiveRaman data sets and the development of a classification model forGleason 7 cancer tissue. In this example, 18 samples from a differentseries of case-control pairs of patients, diagnosed with Gleason 7cancer, were selected for analysis. A case was defined as a patient whohad prostate cancer characterized as Gleason 7 pattern and after theremoval of the prostate went on to later develop metastatic disease. Forpurposes of this application, metastatic Gleason 7 tissue sample will bereferred to as progressive Gleason 7 tissue sample. A control wasdefined as a patient having prostate cancer characterized as Gleason 7pattern and after the removal of the prostate did not later developmetastatic disease. For purposes of this application, a non-metastaticGleason 7 tissue sample will be referred to as non-progressive Gleason 7tissue sample.

Raman spectra were obtained for each case-control pair as discussed forthe Gleason 6 tissue samples in Example 1. Raman spectra were obtainedfrom approximately 20 regions of interest of each tissue section for the9 unstained case-control (progressive-non-progressive) pairs. A total of155 Raman dispersive spectra were obtained for the progressive tissuesamples and a total of 154 Raman dispersive spectra were obtained forthe non-progressive tissue samples. FIG. 12 illustrates the meandispersive Raman spectra 1210 for the case pair tissue samples frompatients with progressive Gleason 7 prostate cancer and the mean Ramanspectra 1220 for the control pair tissue samples from patients withnon-progressive Gleason 7 prostate cancer.

Principal component analysis was applied to the Raman data sets for eachprogressive-non-progressive Gleason 7 pair. FIG. 13 illustrates thepre-determined vector space obtained in this fashion for Gleason 7progressive tissue 1310 and 1350 and Gleason 7 non-progressive tissue1320 and 1340. The points labeled 1310 and 1350 mathematically describethe reference Raman spectra data sets collected for Gleason 7progressive tissue. The points labeled 1320 and 1340 mathematicallydescribe the reference Raman spectra data sets collected for Gleason 7non-progressive tissue. Point 1330 mathematically describes the Ramanspectrum obtained for the Gleason 7 test sample. The vector space, shownin FIG. 13, is a projection of the points in Principal Component spaceonto a single plane. In this projection there is a significant overlapbetween the groups. Though the data sets appear to overlap in thisparticular projection, the classification of a given test Ramanmeasurement can be determined by considering all of the projectionssimultaneously, some of the projections, or even a single projection.

To demonstrate the feasibility of the methods of the present disclosure,each Raman spectrum, for the Gleason 7 tissue samples, was thenclassified as progressive tissue or non-progressive tissue by using aLeave-One-Out cross validation approach wherein a classification model(vector space) was generated with all of the Raman data sets except asingle spectrum (test Raman data set). The classification model thusgenerated was used to classify the one spectrum which was left out. Theprocess was repeated for all spectra. Statistics about how often themodels generated correct results are shown in Table 2. For the 155spectra for the progressive tissue samples, 140 were classifiedcorrectly as progressive and 15 were incorrectly classified asnon-progressive. For the 154 spectra for the non-progressive tissuesamples, 118 were classified correctly as non-progressive and 36 wereincorrectly classified as progressive, as indicated in Table 2. For thisclassification, a sensitivity value of 90% was obtained and aspecificity value of 77% was obtained.

Gleason 7 Gleason 7 Sample Progressive Non-Progressive # of samples 155154 Classified as progressive 140 36 Classified as non-progressive 15118

Example 3

A classification model as to progressive Gleason 7 cancer ornon-progressive Gleason 7 cancer was also developed by extracting Ramanspectra from regions of interest of a tissue sample identified asepithelium, stroma or nuclei tissue. There are different methods whichcan be used to select regions of interest for analysis within a data setrepresentative of a tissue. These include manual selection by an expert(described below), automated selection using characteristics of thedataset itself (e.g. using Spectral Mixture Resolution to identifyregions within the tissue which are epithelium or stroma), automatedselection using analysis of some complimentary set of data (e.g. usingthe UV induced autofluorescence image to select regions of stroma andepithelium). These methods can be used alone or in combination.Moreover, tissue elements different from epithelium and stroma such asthe epithelial-stromal junction (ESJ) can be targeted by similarmethods.

In this example, after consulting a pathologist, regions were drawn onthe image using ChemImage Xpert software tools, specifically the lassotool. The lasso tool enables the user to draw regions on the imagecorresponding to tissue components. The spectra associated with theseregions are then saved and used for PCA analysis. FIG. 14 is an exampleof how this lasso tool is used and how the spectra associated with thoseregions were saved. FIG. 14 illustrates the various tissue areas 1410,1420, 1430, 1440, 1450, 1460, 1470 and 1480 for a selected case.Epithelium regions 1415, 1425 and 1435 were identified for tissuesamples 1410, 1420 and 1430, respectively. Stroma regions 1445, 1465,and 1485 were identified for tissue samples 1440, 1460 and 1480,respectively. Nuclei region 1452 was identified for tissue sample 1450.Nuclei regions 1472, 1474 and 1476 were identified for tissue sample1470.

FIG. 15 shows mean a Raman spectrum 1510 obtained for epithelium areasfor progressive tissue samples and mean Raman spectrum 1520 obtained fornon-progressive tissue samples. The mean spectra were generated from 55spectra of epithelium areas for a progressive tissue sample and from 36spectra of epithelium areas for non-progressive tissue sample. Thespectra show slight differences in the region of 700 to 1800 cm⁻¹. Thesedifferences may reflect the different components of the epitheliumtissue for progressive and non-progressive cancer.

Each spectrum was then classified as non-progressive Gleason 7 cancer orprogressive Gleason 7 using a Leave-One-Out (“LOO”) cross validationapproach. In the LOO approach a model is built by transforming the Ramandata sets into principal component space. All of the data except onemeasurement are used to create the space. Subsequent to the creation ofthe space, the measurement which was left out is classified bytransforming it into the space and evaluating which group it is closestto. This evaluation can be performed using a measure of distance such asthe Mahalanobis distance as used in this case. Alternative methods suchas support vector machines can also be used to divide up the model spaceand determine where within the space the transformed test data lies. Itis important to note that many different choices can be made in terms ofthe construction of a model space. These choices include parameters suchas the number of principal components, wavelength ranges (which do notneed to be contiguous) and others known to those skilled in the art.

As shown in Table 3, for the 55 spectra for the progressive Gleason 7tissue samples, 22 were classified correctly as progressive Gleason 7cancer and 33 were incorrectly classified as non-progressive Gleason 7cancer. For the 36 spectra of the non-progressive tissue samples, 31were classified correctly as non-progressive Gleason 7 cancer and 5 wereincorrectly classified as progressive Gleason 7 cancer. For thisclassification, a sensitivity value of 40% was obtained and aspecificity value of 86% was obtained.

Gleason 7 Gleason 7 Sample Progressive Non-Progressive # of samples 5536 Classified as progressive 22 5 Classified as non-progressive 33 31

This method of extracting Raman spectra from epithelium, stroma ornuclei regions of interest was refined by further subsecting of thedata. This was performed by taking the spectra from epithelium fromregions a pathologist calls Gleason 3 pattern and separating thosespectra from epithelium from regions a pathologist would call a Gleason4 pattern. Thus the epithelium can be divided into two groups based onlocal histology. This results in a group of spectra from epithelium intissues locally consistent with Gleason 3 pattern for both theprogressors and the non-progressors. A similar set of spectra areavailable for Gleason 4 pattern epithelium and Gleason 3 and Gleason 4pattern stroma.

FIG. 16 shows a scatter plot of the spectra from regions of images ofepithelial cells in Gleason 3 pattern areas for progressors 1620 andnon-progressors 1610 in PCA space. Statistical analysis of the x and ycoordinate of this plot shows that the distribution of the progressorsand Non-progressors are distinct to a statistically significant degree.

Leave-One-Out (LOO) analysis as described above yields the resultsbelow, leading to a sensitivity of 95% and a specificity of 91%.

Gleason 7 Gleason 7 Sample Progressive Non-Progressive # of regions ofinterest 58 72 Classified as progressive 53 3 Classified asnon-progressive 5 69In similar fashion the stroma of Gleason 3 pattern areas and also theepithelium and stroma of Gleason 4 pattern areas can be evaluated. Notethat the performance of this approach is significantly better than thewide field dispersive spectra discussed in Example 2, and the imagespectra of epithelium only discussed in Example 3.

As for the Gleason 6 prostate samples, this data shows that Ramanspectroscopy has the capability of detecting differences in progressiveand non-progressive Gleason 7 prostate samples. Based on thesedistinctions, a classification model can be built from wellcharacterized

Example 4

This example illustrates the classification of various areas of a tissuesample by using a spectral mixture resolution algorithm to analyze aRaman image of a test sample. Using a spectral mixture resolutionalgorithm, it is possible to identify regions of interest for spectralselection in lieu of manual selection used in Example 3. This approachrequires reference Raman data sets for a variety of known tissue types,cells or compositions. The concentration images generated in thisexample can also be used as part of a classification scheme. A simpleexample of this approach is to take the ratio of the total amount of aone component to another. This can be performed by, for each component,adding up and appropriately normalizing all the points in theconcentration image for that component (e.g. epithelium fromprogressors) and taking the ratio of this number to the same calculationfor the concentration image of another component (e.g. epithelium fromprogressors). This is not limited to simple ratio as in some casesalgebraic manipulation of the total amounts of the components may bemore indicative of metabolic or disease state.

Another important point in this example is that the components chosenhere are based on histological interpretation (epithelium, stroma, etc.)and disease status (progressive vs. non-progressive) and not oncomponent chemicals (DNA, collagen, etc.), or chemical classes(proteins, lipids etc.). Use of complex components defined based onclinical parameters has the effect of integrating over the details oflocal biochemical interactions and focusing on the desired endpointwhich in this case is metabolic state or disease classification.

FIG. 17 shows the reference Raman spectra for red blood cells (RBC)1710, non-progressive prostate cancer tissue taken from epithelium 1720,progressive prostate cancer tissue taken from epithelium 1730, andstroma tissue 1740 which were used to classify the Raman images.Additional reference Raman spectra may be used for other tissue, cellsor compositions found in sample under analysis.

FIG. 18 shows an image montage 1810, taken at a Raman shift value of2930 cm⁻¹ for a Gleason 7 prostate tissue sample. Because the Ramanfield of view is smaller than the size of the glass slide mounted tissuesample, the sample was divided into twenty areas for Raman datacollection. Spatially accurate wavelength resolved spectra andwavelength resolved spatially accurate images were obtained for each ofthe twenty areas. The final image (an example of a wavelength resolvedspatially accurate image) is composed of 20 regions stitched together toallow visualization of large scale structural features. The image wasanalyzed using a spectral mixture resolution algorithm and the referenceRam an spectra shown in FIG. 17.

FIGS. 19-23 show the results of the analysis of the Gleason 7 prostatetissue sample of FIG. 18 as concentration images. A concentration imageencompasses all of the spectral information, not just one single Ramanshift. Each frame of a concentration image describes where in the fieldof view the reference spectral signal appears. A digital image of theunstained sample is also shown 1920 in each of FIGS. 19-23, along withthe corresponding H&E stained sample that was obtained after the Ramandata was acquired.

The concentration images were generated by standard chemometric tools.The concentration image 1910 of FIG. 19 shows areas of white 1930 whichrepresent stroma tissue. In FIG. 20, the concentration image 2010 showsareas of Gleason grade 3 epithelium non-progressive tissue indicated bythe white areas 2020. FIG. 21 shows a concentration image 2120 whereareas of Gleason grade 3 epithelium progressive tissue are indicated bythe white areas 2120. FIGS. 22 and 23 show concentration images 2220 and2310 where the white areas 2120 indicate blank areas of the glass slideand white areas 2320 indicate red blood cells, respectively. Theseresults show it is not only possible to identify the components of aGleason 7 tissue sample but also to map the sample as to the location ofthe components, in particular the epithelium non-progressive andprogressive tissue.

The results of FIGS. 19-23 were combined to generate a color enhancedmolecular image of the Gleason 7 prostate tissue samples. This colorenhanced image based on the fusion of different component concentrationimages is referred to as a molecular image, or alternatively as a Ramanmolecular image because the contrast which gives rise to the color isdue to the molecular environment of the sample and is detected throughRaman scattering measurements. In the color enhanced molecular image2410 of FIG. 24, the red area 2412 indicates red blood cells, the greenarea 2414 indicates the stroma tissue and the blue area 2416 indicatesthe epithelium from progressive cancer tissue.

Example 5

FIG. 25 illustrates the classification of tissue samples from a Gleason7 case using spectral mixture resolution. Areas in each tissue samplewere classified as red blood cells, non-progressive epithelium tissue,progressive epithelium tissue, or stroma tissue as described in Example4. FIG. 25 shows eight different concentration images for a progressiveGleason 7 prostate sample. Concentration image 2510 illustrates redblood cells by the white areas. Concentration image 2520 illustrateswhite areas representative of non-progressive epithelium cancer tissue.The white areas of concentration image 2530 illustrate progressiveepithelium cancer tissue. Stroma tissue is illustrated by the whiteareas of concentration image 2540. The glass slide is illustrated by thewhite area of concentration image 2550. A stained sample adjacent tissueslice is shown in image 2570 and a Raman image of the tissue sample isshown in image 2580. Concentration image 2560 shows a color enhanceimage to represent the various types of tissue found in the sample wherethe red areas 2562 indicates the location of epithelium fromnon-progressive tissue, the green areas 2564 indicates the location ofstroma tissue, and the blue areas 2566 indicates the location ofepithelium from progressive tissue. As in Example 4, these results showit is possible to identify the presence and location of stroma tissue,blood cells and epithelium tissue for progressive and non-progressivecancer samples.

The methods of the present disclosure may be applicable to a variety ofcancer where Raman scattering indicates a difference between normal andcancer tissue as shown by the following examples which are intended tobe representative and not exhaustive.

Example 6

FIG. 26 illustrates stained images and Raman spectra for various type ofkidney tissue. Image 2610 shows a stained sample of kidney tissueshowing evidence of oncocyte cell and an associated Raman spectrum 2625.Image 2615 shows a stained sample of normal kidney tissue. Ramanspectrum 2635 corresponds to normal kidney tissue. Image 2620 shows astained sample of angiomyolipoma tissue taken from a kidney and itsassociated Raman spectrum 2630.

Example 7

FIG. 27 illustrates stained images and Raman spectra for breast tissue.Image 2710 shows a stained sample of breast tissue characteristic ofductal cancer. The corresponding Raman spectrum is shown by 2730. Ramanspectrum 2625 shows a second spectrum of a cancerous breast tissue.Image 2715 shows an image of normal breast tissue and its associatedRaman spectrum 2720.

Example 8

FIG. 28 illustrated stained images and Raman spectra for lung tissue.Image 2810 shows a stained sample of cancerous lung tissue. Thecorresponding Raman spectrum is shown by 2840. Image 2820 shows an imageof normal lung tissue and its associated Raman spectrum 2840.

The data from Examples 6-8 suggests that differences in the Ramanspectra for the well characterized diseased or non-diseased tissue maybe used to develop a classification model for a disease, includingkidney cancer, breast cancer and/or lung cancer. From thisclassification model, it may be possible to determine whether a testtissue sample is diseased or normal. Furthermore, the differences in thenon-Raman images of diseased or non-diseased tissue may be coupled witha Raman classification model for such determination.

Example 9

FIG. 29 shows Raman spectra for normal brain tissue 2910 andGlioblastoma Multiforme tissue 2920. These spectra show observabledifferences in the region of 1100-1400 cm⁻¹.

Example 10

The example demonstrates the creation of a reference Raman databasehaving reference Raman data sets associated with renal oncocytomadisease (“OC”) and chromophobe renal carcinoma disease (“ChRCC”) asidentified by a pathologist. Studies were conducted on eight (8) OC andeight (8) ChRCC cases of renal tissue. FIG. 35 illustrates bright fieldimages for a OC sample 3520 and ChRCC sample 3510 stained with H & E fortypical histopathologic examination. Regions of interest typical of OCand ChRCC were noted by a pathologist on adjacent H & E stained tissuesections and subsequently located on the unstained tissue sections. Foreach sample, 4-9 regions of interest (“ROI”) of typical OC and ChRCCwere identified for obtaining Raman data sets which were acquired on theChemImage Falcon II™ Imaging System with 532 nm excitation, using 50×microscope objective magnification. The Raman data sets corresponded toa plurality of spatially accurate wavelength resolved images(collectively “Raman images”). A reference data set is illustrated inFIG. 36 for a bright field image 3610 of a ROI of an OC sample fromwhich a Raman image 3620 and a Raman spectrum 3630 were acquired. Notethat the images in FIG. 36 are smaller than the images shown in FIG. 35as the Raman images (one of which is shown in FIG. 35) were collectedfrom fields of view approximately 50 μm in diameter. Raman images werepreprocessed to take into account dark current, instrument response,flat-fielding of the light and baseline corrections. Once preprocessed,a plurality of reference Raman spectra were extracted from Raman imagesby selecting pixel data associated only to epithelial cells within theselected ROI. For each ROI, one reference Raman spectrum was extracted.In total, 57 OC and 57 ChRCC reference Raman spectra were extracted fromthe reference Raman images. Mean Raman spectra were obtained asdescribed in this example, for the OC samples and ChRCC, are illustratedin FIG. 30. The most distinguishing features in the spectra may be inthe “fingerprint” regions, specifically the regions between 900-1155cm⁻¹ (3030) and 1530-1850 cm⁻¹ (3040).

Principal component analysis was applied to the Raman data sets for theeight (8) OC and eight (8) ChRCC cases of renal tissue. FIG. 31illustrates a scatter plot in a pre-determined vector space or aprojection of the vector space onto the two coordinates (PC2 and PC3)for OC tissue and ChRCC tissue. The points labeled 3110 mathematicallydescribe the reference Raman spectra data sets collected for the OCsamples. The points labeled 3120 mathematically describe the referenceRaman spectra data sets collected for the ChRCC samples. With a J3 valueof 3.97, the distinction between the two classes, OC and ChRCC, isclear. This data indicates that Raman imaging may be used to generate aclassification model which may be used to distinguish OC and ChRCCsamples.

The classification model, illustrated in FIG. 34, was then used toclassify four test renal samples using a total of 22 test Raman spectraobtained as described above. The test samples had been classified as two(2) OC and two (2) ChRCC based on histopathological examination. FIG. 37shows the resulting scatter plot, which depicts the model data, aspreviously shown in FIG. 31, and the classification of the test data.The points labeled 3730 and 3740 correspond to samples diagnosed asChRCC and the points labeled 3750 and 3760 correspond to samplesdiagnosed OC. As FIG. 37 illustrates, the model classified the dataassociated with the 4 cases correctly. This data indicates that Ramanimaging may be used to generate a classification model to diagnoseunknown renal samples as corresponding to an OC disease state or a ChRCCdisease state.

The present disclosure may be embodied in other specific forms withoutdeparting from the spirit or essential attributes of the disclosure.Accordingly, reference should be made to the appended claims, ratherthan the foregoing specification, as indicating the scope of thedisclosure. Although the foregoing description is directed to thepreferred embodiments of the disclosure, it is noted that othervariations and modification will be apparent to those skilled in theart, and may be made without departing from the spirit or scope of thedisclosure.

1. A method comprising: providing a group of known renal samples, eachknown renal sample having an associated known renal disease state, saidknown renal disease state including a renal oncocytoma disease state ora chromophobe renal cell carcinoma disease state; obtaining a Raman dataset from each known renal sample; analyzing each Raman data set toidentify a renal oncocytoma reference Raman data set or chromophoberenal cell carcinoma reference Raman data set depending on whetherrespective known renal sample is said renal oncocytoma sample or saidchromophobe renal carcinoma sample; generating a first databasecontaining all renal oncocytoma reference Raman data sets generating asecond database containing all chromophobe renal cell carcinomareference Raman data sets; obtaining a test Raman data set of a testrenal sample having an unknown renal disease state; and providing adiagnostic of whether said test renal sample has a renal oncocytomadisease state or a chromophobe renal carcinoma disease state bycomparing said test Raman data set against reference Raman data sets insaid first reference Raman and said second reference Raman databasesusing a chemometric technique.
 2. A method comprising: providing adatabase containing a plurality of reference Raman data sets, eachreference Raman data set having an associated known renal sample and anassociated known renal disease state; irradiating a test renal samplewith substantially monochromatic light to thereby generate scatteredphotons; collecting a test Raman data set based on said scatteredphotons; comparing said test Raman data set to said plurality ofreference Raman data sets using a chemometric technique; and based onsaid comparing, providing a diagnosis of a renal disease state of thetest renal sample.
 3. The method of claim 2, wherein said plurality ofreference Raman data sets corresponds to one or more of the following: aplurality of reference Raman spectra, each spectrum having an associatedknown renal sample and an associated known renal disease state; and aplurality of reference spatially accurate wavelength resolved Ramanimages, each image having an associated known renal sample and anassociated known renal disease state.
 4. The method of claim 3, whereinsaid known renal disease state includes one of the following: a renaloncocytoma disease state or a chromophobe renal carcinoma disease state.5. The method of claim 4, wherein said plurality of reference Raman datasets comprises a first reference Raman data set having a firstassociated known renal sample having an associated renal oncocytomadisease state, and a second reference Raman data set having a secondassociated known renal sample having an associated chromophobe renalcarcinoma disease state.
 6. The method of claim 5, wherein said firstreference set Raman data set corresponds to one or more of thefollowing: a plurality of first reference Raman spectra having saidfirst associated known renal sample having an associated renaloncocytoma disease state; and a plurality of first reference spatiallyaccurate wavelength resolved Raman images having said first associatedknown renal sample having an associated renal oncocytoma disease state.7. The method of claim 5, wherein said second reference Raman data setscorresponds to one or more of the following: a plurality of secondreference Raman spectra having said second associated known renal samplehaving an associated chromophobe carcinoma disease state; and aplurality of second reference spatially accurate wavelength resolvedRaman images having said second associated known renal sample having anassociated chromophobe carcinoma disease state.
 8. The method of claim2, wherein said known renal sample is one of the following: mammalianrenal cell; and a mammalian renal tissue.
 9. The method of claim 2,wherein said test renal sample is one of the following: a mammalianrenal cell; and a mammalian renal tissue.
 10. The method of claim 2,wherein one of said reference Raman data sets comprises: a plurality ofreference Raman spectra obtained from said one or more regions ofinterest of said known renal sample.
 11. The method of claim 2, whereineach said test Raman data set has at least one of the followingassociated therewith: a corresponding test Raman image; and acorresponding test non-Raman image.
 12. The method of claim 11, furthercomprising: using said test Raman image, identifying one or more regionsof interest of said test renal sample, wherein said one or more regionsof interest contain at least one of the following: an epithelium tissue,a stroma tissue, and a nuclei tissue of said test renal sample.
 13. Themethod of claim 12, wherein said test Raman data set comprises: aplurality of test Raman spectra obtained from said one or more ofregions of interest of said test renal sample.
 14. The method of claim2, wherein said test Raman data set corresponds to one or more of thefollowing: a plurality of Raman spectra of the test renal sample; and aplurality of spatially accurate wavelength resolved Raman images of thetest renal sample.
 15. The method of claim 2, wherein said chemometrictechnique is at least one of the following: Principal ComponentAnalysis, Minimum noise function, spectral mixture resolution, andlinear discriminant analysis.
 16. The method of claim 2, furthercomprising: selecting a pre-determined vector space that mathematicallydescribes said plurality of reference Raman data sets; transforming thetest Raman data set into said pre-determined vector space; and analyzinga distribution of transformed data in the pre-determined vector space soto generate said renal disease state diagnosis.
 17. The method of claim16, wherein said analyzing is performed by using a classificationscheme.
 18. The method of claim 17, wherein said classification schemeis at least one of the following: Mahalanobis distance, Adaptivesubspace detector, Band target entropy method, Neural network, andsupport vector machine.
 19. The method of claim 18, wherein theclassification scheme is Mahalanobis distance, and wherein said methodfurther comprising: calculating a Mahalanobis distance between the testRaman data set transformed into said vector space and the plurality ofreference Raman data sets in said pre-determined vector space so togenerate said renal disease state diagnosis.
 20. A system comprising: areference database containing a plurality of reference Raman data sets,each reference Raman data set having an associated known renal sampleand an associated known renal disease state; an illumination sourceconfigured to illuminate a test renal sample with substantiallymonochromatic light to thereby generate scattered photons; aspectroscopic device configured to collect a test Raman data set basedon said scattered photons; a machine readable program code containingexecutable program instructions; and a processor operatively coupled tothe illumination source and the spectroscopic device, and configured toexecute said machine readable program code so as to perform thefollowing: compare said test Raman data set to said plurality ofreference Raman data sets using a chemometric technique; and based onsaid comparison, diagnosing a renal disease state of the test renalsample.
 21. The system of claim 20, wherein the processor, uponexecuting said machine readable program code, is configured tooperatively control said illumination source and said spectroscopicdevice.
 22. The system of claim 20, wherein said plurality of referenceRaman data sets comprises a first reference Raman data set having afirst associated known renal sample having an associated renaloncocytoma disease state, and a second reference Raman data set having asecond associated known renal sample having an associated chromophoberenal cell carcinoma disease state.
 23. The system of claim 21, whereinsaid spectroscopic device includes an imaging spectrometer.
 24. Thesystem of claim 21, wherein said spectroscopic device includes adispersive spectrometer and a fiber array spectral translator.
 25. Asystem comprising: a database containing a plurality of reference Ramandata sets, each reference Raman data set having an associated knownrenal sample and an associated known renal disease state; means toirradiate a test renal sample with substantially monochromatic light tothereby generate scattered photons; means to collect a test Raman dataset based on said scattered photons; means to compare said test Ramandata set to said plurality of reference Raman data sets using achemometric technique; and based on said comparison, means to diagnose arenal disease state of the test renal sample.
 26. A storage mediumcontaining machine readable program code, which, when executed by aprocessor, causes said processor to perform the following: configure anirradiation source to irradiate a test renal sample with substantiallymonochromatic light to thereby generate scattered photons; configure aspectroscopic device to collect a test Raman data set based on saidscattered photons; compare said test Raman data set to a plurality ofreference Raman data sets using a chemometric technique; and based onsaid comparing, diagnosing a renal disease state of the test renalsample.
 27. The storage medium of claim 26, wherein said machinereadable program code further causes said processor to operativelycontrol said irradiation source and said spectroscopic device.
 28. Amethod comprising: obtaining, at a data generation site, a test Ramandata set from a test renal sample; transmitting said test Raman data setover a data communication network to an analysis center; providing adatabase at said analysis center containing a plurality of referenceRaman data sets, each reference Raman data set having an associatedknown renal sample and an associated known renal disease state;comparing said test Raman data set to said plurality of reference Ramandata sets at said analysis center using a chemometric technique; basedon said comparing, diagnosing a renal disease state of the test renalsample; and transferring said diagnosis to said data generation site viasaid data communication network.
 29. The method of claim 28, whereinsaid data communication network is the Internet.
 30. The method of claim28, wherein said plurality of reference Raman data sets comprises afirst reference Raman data set having a first associated known renalsample having an associated renal oncocytoma disease state, and a secondreference Raman data set having a second associated known renal samplehaving an associated chromophobe renal cell carcinoma disease state. 31.A system comprising: a data generation site having one or morespectroscopic devices; a test Raman data set obtained for a test renalsample at said data generation site; a communication interface whichlinks said data generation site to a data analysis site; a database atsaid analysis site containing a plurality of reference Raman data sets,each reference Raman data set having an associated known renal sampleand an associated known renal disease state; a machine readable programcode at said data analysis site containing executable programinstructions; and a processor at said data analysis site operativelycoupled to said communication interface, and configured to execute saidmachine readable program code so as to perform the following: facilitatetransfer of said test Raman data set from said data generation site tosaid data analysis site via said communication interface; compare saidtest Raman data set to said plurality of reference Raman data sets usinga chemometric technique; and based on said comparison, diagnose a renaldisease state of the test renal sample; and transfer said diagnosis tosaid data generation site via said data communication network.
 32. Thesystem of claim 31, wherein said spectroscopic device includes animaging spectrometer.
 33. The system of claim 31, wherein said pluralityof reference Raman data sets comprises a first reference Raman data sethaving a first associated known renal sample having an associated renaloncocytoma disease state, and a second reference Raman data set having asecond associated known renal sample having an associated chromophoberenal carcinoma disease state.
 34. The system of claim 31, wherein saidspectroscopic device includes a dispersive spectrometer and a fiberarray spectral translator.
 35. The system of claim 31, wherein saidchemometric technique is at least one of the following: PrincipalComponent Analysis, Minimum noise function, spectral mixture resolution,and linear discriminant analysis.